Want to jump to a specific question?
- What is the most popular result?
- Do results vary between countries?
- Are all questions created equal?
What is the most popular result?
Unfortunately, within this data set, Open Psychometrics did not provide us with specific results (e.g. SCUEI), but with a bunch of numbers. In fact, this is what the data looks like:
I’ve only selected the first 6 responses. Not too bad, right? But, you’ve gotta imagine about 50 more columns, and about a million more rows. It’s a little more intimidating now, but we’ve gotta start somewhere >:)
First, let’s calculate the scores for the Extroversion personality trait. We can do this by summing up the scores of the 10 EXT (standing for EXTroversion) questions. Of course, it should be noted that answering a ‘4’ or ‘5’ on a EXT question doesn’t always mean that a user is more extroverted. For example, EXT Question 2: I don’t talk a lot vs EXT Question 1: I am the life of the party.
So, we’ll need to make sure to add and subtract scores as necessary - I’ve arbitrarily made the decision to add ‘points’ if a question symbolizes extroversion, while subtracting ‘points’ if a question is correlated with introversion.
Hence, after adding up all the scores, any positive score (> 0) will mean that the user receives an ‘S’ for Sociable, while a negative score (< 0) would result in an ‘R’. If their score is 0, then they’d receive an ‘X’, as their results can’t really be evaluated since they’re perfectly in-between!
Sample table of results, where extscore and extletter are calculated using the raw data:
Visualizing this data into a graph:
I’m going to refrain from any analysis on WHY there’s more R’s compared to S’s until the final results. But, it’s a pretty 50/50 split!
The same process will be applied to the other personality traits1, with the graphs of results located below:
After getting these values, we can now combine them together to get the results. I’m also going to limit this to the top 25 results… or else the data just gets super messy.
WOOO! Congratulations to the XXOAI family for being among the most common result out of 243 possibilities! If you’d prefer to see a more numerical visual, I’ve provided a table containing the 10 most common results below:
| Results | Amount of Responses | Percentage of People |
|---|---|---|
| SCOAI | 160758 | 15.832892 |
| RLOAI | 132305 | 13.030585 |
| RCOAI | 106355 | 10.474796 |
| RLUAI | 98833 | 9.733962 |
| SLOAI | 96998 | 9.553234 |
| SLUAI | 72098 | 7.100859 |
| SCUAI | 54528 | 5.370407 |
| RCUAI | 40040 | 3.943499 |
| RLXAI | 14370 | 1.415287 |
| SXOAI | 11818 | 1.163943 |
We can also look at the ‘average’ scores of each personality trait.2
| EXT | EST | CSN | AGR | OPN | |
|---|---|---|---|---|---|
| avgscore | 0.48 | -1.066667 | 2.893333 | 14.33333 | 6.4 |
Let’s try to analyze these results.
Extrovertism
There seems to be a nice mix of S(ocial) and R(eserved) values, which is representative of the larger population. It’s the most balanced compared to the other 4 traits, and actually mirror what is expected. This is likely because it’s quite easy for oneself to decide if they’re ‘introverted’ or ‘extroverted’, and the questions (e.g. “I start conversations”) tended to be quite straight-forward and are experiences that they would’ve gone through in their daily life.
When looking at TABLENUM, There are technically about 40000 more R’s than S’s. If I wanted to stereotype (which is an inevitable part of ‘absolute letters’, rather than 70% R vs 30% S), maybe the S’s aren’t as likely to spend 20 minutes on an online test, compared to R’s, who are more likely to be holed up in their room.
Neuroticism
Once again, a solid sample of results between C(alm) and L(imbic). I think it’s similar to extrovertism, where it’s quite easy to determine how strongly you feel your emotions, and your ability to control them.
Scrolling up to TABLENUM, there are more limbic people, compared to calm, by about 60000. I’m not really surprised, in fact, I’d think they’d be more limbic people, as society slowly puts more emphasis on mental health and actually exploring our emotions. This could make us more moody - we actually try to work through our emotions, rather than repressing them, and pretending to be calm. However, it could also be the other way around! By engaging with our emotions, we can better understand ourselves, and lead a life where we are calming since we build control!
Conscientiousness
Same as above, for O(rganized) and U(nstructured). Relatively simple to identify, applicable in daily life.
There’s a lot more O’s than U’s, which I’m also surprised about? There’s also the most X’s in TABLENUM, compared to others. The X’s, I think, can be explained since people fluctuate between having super organized lives, but a messy desk? It’s easy to be both organized and unorganized. As for the large amount of O’s… It’s clearly not represented as much in TABLENUM, considering there’s basically an equal number of each response. It could be because people want to see themselves as organized, or they think they’re organized (e.g. they know where everything is on their desk, but items are actually strewn all over), when in reality, they aren’t! A good thought to carry for the next two personality traits.
Agreeableness
This is where it gets a bit funny! There is not a SINGLE E(gocentric) - it’s just a sea of A(greeable). This is probably because this is a self-administered test, people want to be seen as agreeable in society (correlated with ‘niceness’), and people are biased.
Let me tell you a story that I heard from a TEDTalk. There’s this guy, I think he’s a magician, and he’s at the airport waiting. Something bad has happened that I can’t recall: Maybe his flight has been delayed by 2 hours, or he really needs a refund, or he’s lost his luggage. So, he calls their airline, and the customer representative is clearly in a bad mood, maybe a little bit angry or grouchy, and he’s getting nowhere with his request. The guy hears her voice, and realizes: “Oh, the representative must think that I’M so lucky (despite how she sounds) that I’M talking to her, because she’s taking SO MUCH TIME out of HER day to help me.” So, the guy goes “Hey! I really appreciate you for helping me, and I’m really grateful that I’m talking to you!” The flight attendant INSTANTLY is in a better mood, and becomes genuinely helpful, and ends up solving his request, after he acknowledges her struggles and actually is really kind to the flight attendant, who’s far more used to being yelled at!
So, aside from being a heart-warming story, it also serves to prove two things:
- People are biased. Most of the time, we think that WE’RE the good people, when we objectively aren’t.
- We like being liked. We can show this through helping others, as seen by the flight attendant’s resolve after being appreciated.
So, it makes sense why, if we’re inputting our own answers without requiring proof, people can naturally skew themselves into thinking that they’re nicer than they actually are. I think the same concept applies to agreeability - we like to think that we’re agreeable, because it’s correlated with niceness, and we like being nice. Not only that, but we want to be liked, and oftentimes, by agreeing with others, people have a nicer opinion of us! Lastly, it’s a lot easier to agree with others, than to go against the status quo. Maybe people do disagree, but they’ve just repressed their own thoughts because… society.3 The opposite also applies to egocentrism - nobody wants to call themselves egocentric, as it’s associated with narcissism and self-centeredness. They’re traits that nobody wants. Not only that, but who wants to admit that they agree with “I feel little concern for others.”
Openness
It’s anti-egocentrism part 2, except this time, people are refusing to call themselves non-inquisitive! In fact, it’s even worse than before, if you look at TABLENUM! Once again, it goes back to the same ideas:
- People are biased, and that will significantly skew results if this is a self-administered quiz
- People don’t like being seen as ‘non-inquisitive’, because it often has negative connotations relating to ‘not being smart’ or ‘being uninteresting’. The opposite can be said with people being seen as ‘inquisitive’. Who wants to admit that they’re NOT “quick to understand things.”
However, I think one major point that Openness has that Agreeability doesn’t, is just the fact that taking a Big Five personality quiz, inherently means that you’re going to be more inquisitive. If you weren’t curious about it in the first place, you just… wouldn’t take the quiz or care about the result. Hence, there’s some sample bias, as the people you’re getting results from, are already skewed towards inquisition. In addition, psychology is seen as something ‘nerdy’ and scientific, which caters to ‘smarter’ or ‘more educated’ audiences.
Hence, someone might agree with “I have a rich vocabulary”, not because they’re interested in learning new words, but just because they perceive themselves as having a lot of knowledge due to their education. Or answer a 1 (disagree) to “I have difficulty understanding abstract ideas” or a 5 (agree) to “I am full of ideas” because, through education, they’ve trained themselves to be better at comprehending abstract ideas or becoming better at brainstorming.
Sheer Statistical Impossibility…?
However, When analyzing this data, it seems.. strange that so many XXOAI types are represented. In fact, it feels weird that 15%(!!!!) of people were the EXACT SAME TYPE, despite ALL THE POSSIBILITIES!
So, let’s see how it compares to other, more theoritical, data. Using the data4 from SimilarMinds, we can see how our data stacks up.
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Looking at these tables, there’s a clear discrepancy between theoretical and experimental data, where none of the theoretical results really match up to what is seen in our data set. However, most of it can be explained by the previous analysis: Wrong results might occur because of bias and societal norms (cementing the last two letters to be A and I), in addition to the natural disposition of responders. This comparison really just showcases how there’s a high likelihood of bias.
Honestly, I’m not too sure why 15% of people are SCOAI, maybe people are being influenced to answer what they WANT to be like, rather than what they actually are, since SCOAI seems like one of the most ‘socially-successful’ results. But, in reality, responders aren’t actually SCOAIs. That’s my best guess!5
Also, if you’re interested, here’s a list of the most uncommon results!
| Results | Amount of Responses |
|---|---|
| SLXXX | 1 |
| XLUXX | 1 |
| XLXXX | 1 |
| XXOXX | 1 |
| XXUXN | 1 |
| XXXXN | 1 |
| RXXEX | 2 |
| RXXXX | 2 |
| SXXEN | 2 |
| SXXXN | 2 |
No surprise, it’s a lot of results with X’s. It’s pretty hard to get an X, because you’re perfectly in between!
At first, I was surprised that ‘XXXXX’ didn’t appear, since it’s technically the most unlikely.6 However, I wouldn’t be surprised if only 5% of those results were genuine user responses, and the other 95% were just people who kept clicking 3 (Neutral), or had some kind of game to see if they could perfectly answered the questions to get XXXXX.
Do results vary between countries?
This data from 224 unique locations/contains 224 different ISO country codes.7 Let’s dig through this data - a fun bit of stalking!
We can see that the majority of data came from the US, with a whopping total of 546403 respondents. Followed behind them is Great Britain (GB), Canada (CA), and Australia (Au). This is likely because this quiz is in English, and will generally cater towards countries with English as their primary language.
Google’s SEO (Search Engine Optimization) is also affected by location, and can rank websites by their proximity to the user8. So, it’s possible that Open Psychometrics is American9, and when Americans search up “Big Five Personality Test”, this is the first quiz that shows up.10
But, we’re not really concerned on WHERE people are taking the quiz, but how it affects answers. Hence, I’m going to find the top 5 most popular results for America, Great Britain, Australia, and the Philippians–top results from different continents–and seeing how they compare to one another.
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From this table, it’s pretty clear that the countries generally have similar results, but Philippines is the only one that really stands out from the rest. Namely, the country has more RLAOI than SCOAI, meaning respondents tended to be more limbic and introverted than their counterparts. It’s difficult to point out why without knowing the average age of the respondents, as that could dictate a lot of their behaviors.
However, this also shows these countries follow the trend - every single one of the results were inside the top 25. Of course, this is the expected result, but one can always wish for a little special surprise, you know?
Let’s try zooming out.
Are All Questions Created Equal?
We can also analyze the questions themselves. A funky little thing that this quiz did, was record how many milliseconds each respondent spent on each question. That seems like a fun thing to look at….
Thus, I present to you: General time spent on each question!
This is a good visual to compare questions to each other, especially questions in the same category.
For your reference:
- EXT = Extroversion (S/R)
- EST = Neuroticsm (L/C)
- CSN = Organization (O/U)
- AGR = Agreeableness (A/E)
- OPN = Openness (I/U)
For those unaware, this is a box plot11. The white box symbolizes the interquartile range, with the lower half of the box showing the bottom 25%, while the top half of the box shows the top 25%. The line in the center is the median of the question. It’s really good for visualizing the spread of data (which seems to range quite a bit). However, I’ve skewed the data by cutting off any values above 20 seconds12 and only took a random sample of 500 response times for each question13.
However, I want to be more precise with these loading times, so I’ve specifically pulled the questions that take the longest and shortest times to complete.
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I’ve taken the liberty of removing EXT_1 (the first question on the quiz), which had an average score of 87 seconds. This is likely because people people were getting accustomed to the quiz, had loading time issues, or started the quiz, then immediately forgot about it.
“I shirk my duties” took the longest, likely because ‘shirk’ is an uncommon word and people wanted to search up the definition, which easily could’ve taken an extra 10 seconds. As for the other questions, they’re all relatively long sentences and have more nuance. They’re all very situational, as people are rarely “always relaxed” or “never relaxed”, and it takes time to think whether you’re relax a MAJOURITY of the time. Same thing for little concern for others - there are tons of people you care about, but also, tons of people you don’t. So, it’s a bit tricky to answer those.
On the other hand, the questions that took the least amount of time are short and straightforward, and are obvious to judge yourself on. You either read a dictionary for fun to seem smart, or you don’t. I do find it interesting that it’s difficult for people to determine whether they’re ‘relaxed’ rather than ‘blue’. Likely because you conciously pay attention to when you’re sad, but not when you’re relaxed? You can also see that these questions are the ones that are asked later (e.g. AGR_9 is part of the 9th out of 10 rounds of questions, meaning that it’ll be really close to the ending of the quiz). Anticipation for results may have caused them to rush through the later questions.
Another interesting thing to note is the distrubtion of answers for each question. You may expected that each question has a distribution similar to standard deviation. However… that’s actually not the case! Many questions tend to have a distribution like so:
There are four major types of distribution seen in the data:
- Logarithmic (First Row)
The columns progressively increase or decrease. I think these graphs are the most “trustworthy”, as you’d only put 1 or 5 (the extremes) if you were incredibly confident in your answer.14 Not only that, but these questions aren’t really ‘shameful’ to admit, like “I get stressed out easily”, which allows people to be honest and pick extremes. Questions also tend to be less ‘situation’ and more ‘specific’, where you can very clearly visualize what you’d be doing in that situation, rather than responding “Oh… sometimes I am, sometimes I’m not!” (E.g. “I am quiet” likely wouldn’t follow this trend, but “I am quiet around strangers” probably does, because it helps narrow down the situation. You also don’t really change your behavior around different strangers, you usually are pretty constant with your behavior.)
Some other examples include:
- I have difficulty understanding abstract ideas.
- I am quiet around strangers.
- I have a soft heart.
- I spend time reflecting on things.
If you’re wondering which side they skew on…. just trust your gut on it :)
- Skewed (Second Row)
i feel like these are best associated with questions where self-bias is most prevalent, as you want to make yourself as something you’re not (to make yourself feel better). These questions are also very situational; Sometimes I do this, sometimes I do. That’s why people tend to move towards the middle. This is the most common distribution type.
Examples include:
- I make a mess of things.
- I get stressed out easily.
- I leave my belongings around.
- I follow a schedule.
- “Normal” Distribution (Third Row, Left)
I’m lying to you - This graph isn’t normal distribution. I’m just calling it normal distribution because the answer 3 (neutral) is the most common answer. Quite frankly, it just means that people are either confused, or they don’t really have a large opinion on it, so they’re almost forced to choose 3. These questions seem to be the most ‘observable/objective’ of the bunch. Personally, I try to make a habit on avoiding clicking 3 no matter what (I’m not sure if others are the same), but it’s still interesting to see. I feel like these things are not things to be ‘proud’ of or dislike about yourself, nor would you mention it unless prompted, which is likely why it’s associated with the extrovertism questions.
Examples Include:
- I don’t talk a lot.
- I keep in the background.
- Relatively Random (Third Row, Right)
So these are the ones that don’t follow a trend. Well, what happens is that 1 and 5 are similar in height and have about 150k responses, while 3, 4, 5 are also similar in height, with about 250k responses. It’s the kinds of questions that go “Yea… I’m definitely not SUPER ___, but it happens from time to time… I’m not sure how I would compare with other people though… so 2, 3, or 4 sound about right.”
Examples Include:
- I talk to a lot of different people at parties.
- I am easily disturbed.
- I often forget to put things back in their proper place.15
- I change my mood a lot.
Longitude Latitude group order region subregion
1 -69.89912 12.45200 1 1 AW <NA>
2 -69.89571 12.42300 1 2 AW <NA>
3 -69.94219 12.43853 1 3 AW <NA>
4 -70.00415 12.50049 1 4 AW <NA>
5 -70.06612 12.54697 1 5 AW <NA>
6 -70.05088 12.59707 1 6 AW <NA>
7 -70.03511 12.61411 1 7 AW <NA>
8 -69.97314 12.56763 1 8 AW <NA>
9 -69.91181 12.48047 1 9 AW <NA>
10 -69.89912 12.45200 1 10 AW <NA>
12 74.89131 37.23164 2 12 AF <NA>
13 74.84023 37.22505 2 13 AF <NA>
14 74.76738 37.24917 2 14 AF <NA>
15 74.73896 37.28564 2 15 AF <NA>
16 74.72666 37.29072 2 16 AF <NA>
17 74.66895 37.26670 2 17 AF <NA>
18 74.55899 37.23662 2 18 AF <NA>
19 74.37217 37.15771 2 19 AF <NA>
20 74.37617 37.13735 2 20 AF <NA>
21 74.49796 37.05722 2 21 AF <NA>
22 74.52646 37.03066 2 22 AF <NA>
23 74.54140 37.02217 2 23 AF <NA>
24 74.43106 36.98369 2 24 AF <NA>
25 74.19473 36.89688 2 25 AF <NA>
26 74.03887 36.82573 2 26 AF <NA>
27 74.00185 36.82310 2 27 AF <NA>
28 73.90781 36.85293 2 28 AF <NA>
29 73.76914 36.88848 2 29 AF <NA>
30 73.73183 36.88779 2 30 AF <NA>
31 73.41113 36.88169 2 31 AF <NA>
32 73.11680 36.86856 2 32 AF <NA>
33 72.99374 36.85161 2 33 AF <NA>
34 72.76621 36.83501 2 34 AF <NA>
35 72.62286 36.82959 2 35 AF <NA>
36 72.53135 36.80200 2 36 AF <NA>
37 72.43115 36.76582 2 37 AF <NA>
38 72.32696 36.74239 2 38 AF <NA>
39 72.24980 36.73472 2 39 AF <NA>
40 72.15674 36.70088 2 40 AF <NA>
41 72.09560 36.63374 2 41 AF <NA>
42 71.92070 36.53418 2 42 AF <NA>
43 71.82227 36.48608 2 43 AF <NA>
44 71.77266 36.43184 2 44 AF <NA>
45 71.71641 36.42656 2 45 AF <NA>
46 71.62051 36.43647 2 46 AF <NA>
47 71.54590 36.37769 2 47 AF <NA>
48 71.46328 36.29326 2 48 AF <NA>
49 71.31260 36.17119 2 49 AF <NA>
50 71.23291 36.12178 2 50 AF <NA>
51 71.18506 36.04209 2 51 AF <NA>
52 71.22021 36.00068 2 52 AF <NA>
53 71.34287 35.93853 2 53 AF <NA>
54 71.39756 35.88018 2 54 AF <NA>
55 71.42754 35.83374 2 55 AF <NA>
56 71.48359 35.71460 2 56 AF <NA>
57 71.51904 35.59751 2 57 AF <NA>
58 71.57198 35.54683 2 58 AF <NA>
59 71.58740 35.46084 2 59 AF <NA>
60 71.60059 35.40791 2 60 AF <NA>
61 71.57198 35.37041 2 61 AF <NA>
62 71.54551 35.32851 2 62 AF <NA>
63 71.54551 35.28886 2 63 AF <NA>
64 71.57725 35.24800 2 64 AF <NA>
65 71.60527 35.21177 2 65 AF <NA>
66 71.62051 35.18301 2 66 AF <NA>
67 71.60166 35.15068 2 67 AF <NA>
68 71.54551 35.10141 2 68 AF <NA>
69 71.51709 35.05112 2 69 AF <NA>
70 71.45508 34.96694 2 70 AF <NA>
71 71.35811 34.90962 2 71 AF <NA>
72 71.29414 34.86773 2 72 AF <NA>
73 71.22578 34.77954 2 73 AF <NA>
74 71.11328 34.68159 2 74 AF <NA>
75 71.06563 34.59961 2 75 AF <NA>
76 71.01631 34.55464 2 76 AF <NA>
77 70.96562 34.53037 2 77 AF <NA>
78 70.97891 34.48628 2 78 AF <NA>
79 71.02295 34.43115 2 79 AF <NA>
80 71.09570 34.36943 2 80 AF <NA>
81 71.09238 34.27324 2 81 AF <NA>
82 71.08906 34.20406 2 82 AF <NA>
83 71.09131 34.12027 2 83 AF <NA>
84 71.05156 34.04971 2 84 AF <NA>
85 70.84844 33.98188 2 85 AF <NA>
86 70.65401 33.95229 2 86 AF <NA>
87 70.41573 33.95044 2 87 AF <NA>
88 70.32568 33.96113 2 88 AF <NA>
89 70.25361 33.97598 2 89 AF <NA>
90 69.99473 34.05181 2 90 AF <NA>
91 69.88965 34.00727 2 91 AF <NA>
92 69.86806 33.89766 2 92 AF <NA>
93 70.05664 33.71987 2 93 AF <NA>
94 70.13418 33.62075 2 94 AF <NA>
95 70.21973 33.45469 2 95 AF <NA>
96 70.28418 33.36904 2 96 AF <NA>
97 70.26113 33.28901 2 97 AF <NA>
98 70.09023 33.19810 2 98 AF <NA>
99 69.92012 33.11250 2 99 AF <NA>
100 69.70371 33.09473 2 100 AF <NA>
101 69.56777 33.06416 2 101 AF <NA>
102 69.50156 33.02007 2 102 AF <NA>
103 69.45312 32.83281 2 103 AF <NA>
104 69.40459 32.76426 2 104 AF <NA>
105 69.40537 32.68272 2 105 AF <NA>
106 69.35947 32.59033 2 106 AF <NA>
107 69.28994 32.53057 2 107 AF <NA>
108 69.24140 32.43354 2 108 AF <NA>
109 69.25654 32.24946 2 109 AF <NA>
110 69.27930 31.93682 2 110 AF <NA>
111 69.18691 31.83809 2 111 AF <NA>
112 69.08311 31.73848 2 112 AF <NA>
113 68.97343 31.66738 2 113 AF <NA>
114 68.86895 31.63423 2 114 AF <NA>
115 68.78233 31.64643 2 115 AF <NA>
116 68.71367 31.70806 2 116 AF <NA>
117 68.67324 31.75972 2 117 AF <NA>
118 68.59766 31.80298 2 118 AF <NA>
119 68.52071 31.79414 2 119 AF <NA>
120 68.44326 31.75449 2 120 AF <NA>
121 68.31982 31.76767 2 121 AF <NA>
122 68.21397 31.80737 2 122 AF <NA>
123 68.16103 31.80298 2 123 AF <NA>
124 68.13017 31.76328 2 124 AF <NA>
125 68.01719 31.67798 2 125 AF <NA>
126 67.73985 31.54819 2 126 AF <NA>
127 67.62675 31.53877 2 127 AF <NA>
128 67.57822 31.50649 2 128 AF <NA>
129 67.59756 31.45332 2 129 AF <NA>
130 67.64706 31.40996 2 130 AF <NA>
131 67.73350 31.37925 2 131 AF <NA>
132 67.73789 31.34395 2 132 AF <NA>
133 67.66152 31.31299 2 133 AF <NA>
134 67.59639 31.27769 2 134 AF <NA>
135 67.45283 31.23462 2 135 AF <NA>
136 67.28730 31.21782 2 136 AF <NA>
137 67.11592 31.24292 2 137 AF <NA>
138 67.02773 31.30024 2 138 AF <NA>
139 66.92432 31.30561 2 139 AF <NA>
140 66.82929 31.26367 2 140 AF <NA>
141 66.73135 31.19453 2 141 AF <NA>
142 66.62422 31.04604 2 142 AF <NA>
143 66.59580 31.01997 2 143 AF <NA>
144 66.56680 30.99658 2 144 AF <NA>
145 66.49736 30.96455 2 145 AF <NA>
146 66.39717 30.91221 2 146 AF <NA>
147 66.34687 30.80278 2 147 AF <NA>
148 66.28691 30.60791 2 148 AF <NA>
149 66.30097 30.50298 2 149 AF <NA>
150 66.30547 30.32114 2 150 AF <NA>
151 66.28184 30.19346 2 151 AF <NA>
152 66.23848 30.10962 2 152 AF <NA>
153 66.24717 30.04351 2 153 AF <NA>
154 66.31338 29.96856 2 154 AF <NA>
155 66.28691 29.92002 2 155 AF <NA>
156 66.23125 29.86572 2 156 AF <NA>
157 66.17706 29.83559 2 157 AF <NA>
158 65.96162 29.77891 2 158 AF <NA>
159 65.66621 29.70132 2 159 AF <NA>
160 65.47099 29.65156 2 160 AF <NA>
161 65.18047 29.57763 2 161 AF <NA>
162 65.09550 29.55947 2 162 AF <NA>
163 64.91895 29.55278 2 163 AF <NA>
164 64.82734 29.56416 2 164 AF <NA>
165 64.70351 29.56714 2 165 AF <NA>
166 64.52110 29.56450 2 166 AF <NA>
167 64.39375 29.54433 2 167 AF <NA>
[ reached 'max' / getOption("max.print") -- omitted 99172 rows ] Longitude Latitude group order region subregion ExtScores
1 -69.89912 12.45200 1 1 AW <NA> -0.7647059
2 -69.89571 12.42300 1 2 AW <NA> -0.7647059
3 -69.94219 12.43853 1 3 AW <NA> -0.7647059
4 -70.00415 12.50049 1 4 AW <NA> -0.7647059
5 -70.06612 12.54697 1 5 AW <NA> -0.7647059
6 -70.05088 12.59707 1 6 AW <NA> -0.7647059
7 -70.03511 12.61411 1 7 AW <NA> -0.7647059
8 -69.97314 12.56763 1 8 AW <NA> -0.7647059
9 -69.91181 12.48047 1 9 AW <NA> -0.7647059
10 -69.89912 12.45200 1 10 AW <NA> -0.7647059
11 74.89131 37.23164 2 12 AF <NA> 1.0185185
12 74.84023 37.22505 2 13 AF <NA> 1.0185185
13 74.76738 37.24917 2 14 AF <NA> 1.0185185
14 74.73896 37.28564 2 15 AF <NA> 1.0185185
15 74.72666 37.29072 2 16 AF <NA> 1.0185185
16 74.66895 37.26670 2 17 AF <NA> 1.0185185
17 74.55899 37.23662 2 18 AF <NA> 1.0185185
18 74.37217 37.15771 2 19 AF <NA> 1.0185185
19 74.37617 37.13735 2 20 AF <NA> 1.0185185
20 74.49796 37.05722 2 21 AF <NA> 1.0185185
21 74.52646 37.03066 2 22 AF <NA> 1.0185185
22 74.54140 37.02217 2 23 AF <NA> 1.0185185
23 74.43106 36.98369 2 24 AF <NA> 1.0185185
24 74.19473 36.89688 2 25 AF <NA> 1.0185185
25 74.03887 36.82573 2 26 AF <NA> 1.0185185
26 74.00185 36.82310 2 27 AF <NA> 1.0185185
27 73.90781 36.85293 2 28 AF <NA> 1.0185185
28 73.76914 36.88848 2 29 AF <NA> 1.0185185
29 73.73183 36.88779 2 30 AF <NA> 1.0185185
30 73.41113 36.88169 2 31 AF <NA> 1.0185185
31 73.11680 36.86856 2 32 AF <NA> 1.0185185
32 72.99374 36.85161 2 33 AF <NA> 1.0185185
33 72.76621 36.83501 2 34 AF <NA> 1.0185185
34 72.62286 36.82959 2 35 AF <NA> 1.0185185
35 72.53135 36.80200 2 36 AF <NA> 1.0185185
36 72.43115 36.76582 2 37 AF <NA> 1.0185185
37 72.32696 36.74239 2 38 AF <NA> 1.0185185
38 72.24980 36.73472 2 39 AF <NA> 1.0185185
39 72.15674 36.70088 2 40 AF <NA> 1.0185185
40 72.09560 36.63374 2 41 AF <NA> 1.0185185
41 71.92070 36.53418 2 42 AF <NA> 1.0185185
42 71.82227 36.48608 2 43 AF <NA> 1.0185185
43 71.77266 36.43184 2 44 AF <NA> 1.0185185
44 71.71641 36.42656 2 45 AF <NA> 1.0185185
45 71.62051 36.43647 2 46 AF <NA> 1.0185185
46 71.54590 36.37769 2 47 AF <NA> 1.0185185
47 71.46328 36.29326 2 48 AF <NA> 1.0185185
48 71.31260 36.17119 2 49 AF <NA> 1.0185185
49 71.23291 36.12178 2 50 AF <NA> 1.0185185
50 71.18506 36.04209 2 51 AF <NA> 1.0185185
51 71.22021 36.00068 2 52 AF <NA> 1.0185185
52 71.34287 35.93853 2 53 AF <NA> 1.0185185
53 71.39756 35.88018 2 54 AF <NA> 1.0185185
54 71.42754 35.83374 2 55 AF <NA> 1.0185185
55 71.48359 35.71460 2 56 AF <NA> 1.0185185
56 71.51904 35.59751 2 57 AF <NA> 1.0185185
57 71.57198 35.54683 2 58 AF <NA> 1.0185185
58 71.58740 35.46084 2 59 AF <NA> 1.0185185
59 71.60059 35.40791 2 60 AF <NA> 1.0185185
60 71.57198 35.37041 2 61 AF <NA> 1.0185185
61 71.54551 35.32851 2 62 AF <NA> 1.0185185
62 71.54551 35.28886 2 63 AF <NA> 1.0185185
63 71.57725 35.24800 2 64 AF <NA> 1.0185185
64 71.60527 35.21177 2 65 AF <NA> 1.0185185
65 71.62051 35.18301 2 66 AF <NA> 1.0185185
66 71.60166 35.15068 2 67 AF <NA> 1.0185185
67 71.54551 35.10141 2 68 AF <NA> 1.0185185
68 71.51709 35.05112 2 69 AF <NA> 1.0185185
69 71.45508 34.96694 2 70 AF <NA> 1.0185185
70 71.35811 34.90962 2 71 AF <NA> 1.0185185
71 71.29414 34.86773 2 72 AF <NA> 1.0185185
72 71.22578 34.77954 2 73 AF <NA> 1.0185185
73 71.11328 34.68159 2 74 AF <NA> 1.0185185
74 71.06563 34.59961 2 75 AF <NA> 1.0185185
75 71.01631 34.55464 2 76 AF <NA> 1.0185185
76 70.96562 34.53037 2 77 AF <NA> 1.0185185
77 70.97891 34.48628 2 78 AF <NA> 1.0185185
78 71.02295 34.43115 2 79 AF <NA> 1.0185185
79 71.09570 34.36943 2 80 AF <NA> 1.0185185
80 71.09238 34.27324 2 81 AF <NA> 1.0185185
81 71.08906 34.20406 2 82 AF <NA> 1.0185185
82 71.09131 34.12027 2 83 AF <NA> 1.0185185
83 71.05156 34.04971 2 84 AF <NA> 1.0185185
84 70.84844 33.98188 2 85 AF <NA> 1.0185185
85 70.65401 33.95229 2 86 AF <NA> 1.0185185
86 70.41573 33.95044 2 87 AF <NA> 1.0185185
87 70.32568 33.96113 2 88 AF <NA> 1.0185185
88 70.25361 33.97598 2 89 AF <NA> 1.0185185
89 69.99473 34.05181 2 90 AF <NA> 1.0185185
90 69.88965 34.00727 2 91 AF <NA> 1.0185185
91 69.86806 33.89766 2 92 AF <NA> 1.0185185
92 70.05664 33.71987 2 93 AF <NA> 1.0185185
93 70.13418 33.62075 2 94 AF <NA> 1.0185185
94 70.21973 33.45469 2 95 AF <NA> 1.0185185
95 70.28418 33.36904 2 96 AF <NA> 1.0185185
96 70.26113 33.28901 2 97 AF <NA> 1.0185185
97 70.09023 33.19810 2 98 AF <NA> 1.0185185
98 69.92012 33.11250 2 99 AF <NA> 1.0185185
99 69.70371 33.09473 2 100 AF <NA> 1.0185185
100 69.56777 33.06416 2 101 AF <NA> 1.0185185
101 69.50156 33.02007 2 102 AF <NA> 1.0185185
102 69.45312 32.83281 2 103 AF <NA> 1.0185185
103 69.40459 32.76426 2 104 AF <NA> 1.0185185
104 69.40537 32.68272 2 105 AF <NA> 1.0185185
105 69.35947 32.59033 2 106 AF <NA> 1.0185185
106 69.28994 32.53057 2 107 AF <NA> 1.0185185
107 69.24140 32.43354 2 108 AF <NA> 1.0185185
108 69.25654 32.24946 2 109 AF <NA> 1.0185185
109 69.27930 31.93682 2 110 AF <NA> 1.0185185
110 69.18691 31.83809 2 111 AF <NA> 1.0185185
111 69.08311 31.73848 2 112 AF <NA> 1.0185185
112 68.97343 31.66738 2 113 AF <NA> 1.0185185
113 68.86895 31.63423 2 114 AF <NA> 1.0185185
114 68.78233 31.64643 2 115 AF <NA> 1.0185185
115 68.71367 31.70806 2 116 AF <NA> 1.0185185
116 68.67324 31.75972 2 117 AF <NA> 1.0185185
117 68.59766 31.80298 2 118 AF <NA> 1.0185185
118 68.52071 31.79414 2 119 AF <NA> 1.0185185
119 68.44326 31.75449 2 120 AF <NA> 1.0185185
120 68.31982 31.76767 2 121 AF <NA> 1.0185185
121 68.21397 31.80737 2 122 AF <NA> 1.0185185
122 68.16103 31.80298 2 123 AF <NA> 1.0185185
123 68.13017 31.76328 2 124 AF <NA> 1.0185185
124 68.01719 31.67798 2 125 AF <NA> 1.0185185
125 67.73985 31.54819 2 126 AF <NA> 1.0185185
126 67.62675 31.53877 2 127 AF <NA> 1.0185185
127 67.57822 31.50649 2 128 AF <NA> 1.0185185
128 67.59756 31.45332 2 129 AF <NA> 1.0185185
129 67.64706 31.40996 2 130 AF <NA> 1.0185185
130 67.73350 31.37925 2 131 AF <NA> 1.0185185
131 67.73789 31.34395 2 132 AF <NA> 1.0185185
132 67.66152 31.31299 2 133 AF <NA> 1.0185185
133 67.59639 31.27769 2 134 AF <NA> 1.0185185
134 67.45283 31.23462 2 135 AF <NA> 1.0185185
135 67.28730 31.21782 2 136 AF <NA> 1.0185185
136 67.11592 31.24292 2 137 AF <NA> 1.0185185
137 67.02773 31.30024 2 138 AF <NA> 1.0185185
138 66.92432 31.30561 2 139 AF <NA> 1.0185185
139 66.82929 31.26367 2 140 AF <NA> 1.0185185
140 66.73135 31.19453 2 141 AF <NA> 1.0185185
141 66.62422 31.04604 2 142 AF <NA> 1.0185185
142 66.59580 31.01997 2 143 AF <NA> 1.0185185
[ reached 'max' / getOption("max.print") -- omitted 94967 rows ]# A tibble: 217 × 2
region CsnScores
<chr> <dbl>
1 AD 2.29
2 AE 2.77
3 AF 3.17
4 AG 2.54
5 AI 3.75
6 AL 1.60
7 AM 1.79
8 AO -0.5
9 AQ -0.5
10 AR 0.447
# ℹ 207 more rowsThe neuroticism table was actually calculated differently. For the other 4 traits, there were an equal number of ‘positive (e.g. extroversion) questions’ and ‘negative (e.g. introversion) questions.’ Neuroticism had 2 ‘positive’ questions and 8 negative questions. So, subtracted 3 from each result, where a score of 5 (Agree) became +2, and a score of 1 (negative) became -2. This helped balance the questions, while using a similar process. I tried using this method with the other tables and got the same values as my previous method, which is good to see!↩︎
The average values will actually shift every time you refresh this webpage, as I found a function to randomly grab 100 people, then take the average!↩︎
I use agreeableness pretty synonymously with niceness. One can critique that isn’t the case, and that agreeability and niceness are more distinct than I make them out to be. That can absolutely be the case, and the story is not relevant. However, the point about society preferring agreeability still stands, and society will still influence how you act and how you perceive yourself.↩︎
This was the only site that had theoretical values. I don’t know where they got their percentages from, and there was also no data on certain combinations. It should also be noted that this site does not use ‘X’ as a possible result. E.g. XLUEI (or any combination with an ‘X’) is not considered. So this source can not be completely trusted. It should also be noted that SimilarMinds separated the theoretical values by female and male. Since this data set does not have this distinction, I used the average of the male and female theoretical values.↩︎
If you have more ideas, I’d love to hear them! Reach out :)↩︎
There’s actually 3794 results for XXXXX↩︎
‘Country codes’ are kinda misleading. There are ~195 countries, and ISO has 249 different codes. This is because the ISO contains subdivisions of countries, e.g. Caymen Islands (UK), Christmas Island (Australia), and the Holy See.↩︎
https://www.searchenginejournal.com/ranking-factors/physical-proximity-to-searcher↩︎
I couldn’t find any location data on the website itself↩︎
As a Canadian, it’s actually the third search result!↩︎
I actually forgot this type of graph existed, until I was randomly scrolling through the ggplot2 library of different graph types!↩︎
as it’s quite uncommon and makes plotting the graph difficult↩︎
500 is enough to show the general trend, and utilizing more only makes graphs take longer to load.↩︎
If you ask people to choose a number between 1 and 5, they tend to pick 2, 3, or 4, rather than 1 or 5.↩︎
This one is almost inverted, where 2 and 4 are the most common responses, followed by 1 and 5, then 3.↩︎